app.py

import os
import gradio as gr
import requests
from PIL import Image
import io
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Get API key from environment variable
# Set your API key in Hugging Face Space Settings > Variables and Secrets
API_KEY = os.getenv("API_KEY")

# NEW CORRECT ENDPOINT (updated from api-inference.huggingface.co)
API_URL = os.getenv("API_URL", "https://router.huggingface.co/hf-inference/models/black-forest-labs/FLUX.1-schnell")

def generate_image(prompt, negative_prompt="", width=1024, height=1024, guidance_scale=7.5, num_inference_steps=50):
    """
    Generate image using Hugging Face Inference API (Router endpoint)
    """
    if not API_KEY:
        return None, "Error: API key not configured. Please set the API_KEY in your Space secrets."
    
    if not prompt:
        return None, "Error: Please enter a prompt."
    
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    # FLUX.1-schnell uses different parameters - adjust payload accordingly
    payload = {
        "inputs": prompt,
        "parameters": {
            "width": width,
            "height": height,
            "guidance_scale": guidance_scale,
            "num_inference_steps": num_inference_steps,
        }
    }
    
    # Only add negative_prompt if provided and model supports it
    if negative_prompt:
        payload["parameters"]["negative_prompt"] = negative_prompt
    
    try:
        response = requests.post(API_URL, headers=headers, json=payload, timeout=300)
        
        if response.status_code != 200:
            return None, f"API Error: {response.status_code} - {response.text}"
        
        # Handle image response
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        return image, "Success!"
        
    except Exception as e:
        return None, f"Error: {str(e)}"

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎨 AI Image Generator
    
    Generate stunning images from text prompts using AI. Enter your prompt below and click Generate.
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            prompt_input = gr.Textbox(
                label="Prompt",
                placeholder="A serene landscape with mountains and a lake at sunset...",
                lines=3
            )
            
            negative_prompt_input = gr.Textbox(
                label="Negative Prompt (what to avoid)",
                placeholder="blurry, low quality, distorted...",
                lines=2
            )
            
            with gr.Row():
                width_slider = gr.Slider(
                    minimum=512,
                    maximum=1024,
                    step=64,
                    value=1024,
                    label="Width"
                )
                height_slider = gr.Slider(
                    minimum=512,
                    maximum=1024,
                    step=64,
                    value=1024,
                    label="Height"
                )
            
            guidance_slider = gr.Slider(
                minimum=1,
                maximum=20,
                step=0.5,
                value=7.5,
                label="Guidance Scale"
            )
            
            steps_slider = gr.Slider(
                minimum=1,
                maximum=50,
                step=1,
                value=4,
                label="Inference Steps (FLUX works well with 4-20)"
            )
            
            generate_btn = gr.Button("✨ Generate Image", variant="primary")
            status_text = gr.Textbox(label="Status", interactive=False)
        
        with gr.Column(scale=1):
            output_image = gr.Image(label="Generated Image", type="pil")
            
            gr.Markdown("""
            ### Tips for better results:
            - Be specific and descriptive in your prompts
            - Include style keywords: "digital art", "photorealistic", "oil painting"
            - Mention lighting: "cinematic lighting", "golden hour", "studio lighting"
            - FLUX models work great with just 4-8 inference steps!
            """)
    
    # Event handlers
    generate_btn.click(
        fn=generate_image,
        inputs=[
            prompt_input,
            negative_prompt_input,
            width_slider,
            height_slider,
            guidance_slider,
            steps_slider
        ],
        outputs=[output_image, status_text]
    )
    
    # Examples
    gr.Examples(
        examples=[
            ["A futuristic city at night with neon lights and flying cars, digital art", "", 1024, 1024, 7.5, 4],
            ["Portrait of a wise wizard with long beard, magical atmosphere, oil painting", "blurry, ugly", 1024, 1024, 8, 4],
            ["Cute robot reading a book in a cozy library, pixar style", "", 1024, 1024, 7, 4],
        ],
        inputs=[prompt_input, negative_prompt_input, width_slider, height_slider, guidance_slider, steps_slider],
        label="Example Prompts"
    )

if __name__ == "__main__":
    demo.launch()